The overuse of antibiotics and antitumor drugs has resulted in more and more extensive pollution of water bodies with organic drugs,causing detrimental ecological effects,which have attracted attention towards effecti...The overuse of antibiotics and antitumor drugs has resulted in more and more extensive pollution of water bodies with organic drugs,causing detrimental ecological effects,which have attracted attention towards effective and sustainable methods for antibiotics and antitumor drug degradation.Here,the hybrid nanomaterial(g-C_(3)N_(4)@Fe/Pd)was synthesized and used to remove a kind of both an antibiotic and antitumor drug named mitoxantrone(MTX)with 92.0%removal efficiency,and the MTX removal capacity is 450 mg/g.After exposing to the hybrid material the MTX aqueous solution changed color from dark blue to lighter progressively,and LC-UV results of residual solutions showthat a newpeak at 3.0min(MTX:13.2min)after removal by g-C_(3)N_(4)@Fe/Pd appears,with the simultaneous detection of intermediate products indicating that g-C_(3)N_(4)@Fe/Pd indeed degrades MTX.Detailed mass spectrometric analysis suggests that the nuclear mass ratio decreased from 445.2(M+1H)to 126.0(M+1H),169.1(M+1H),239.2(M+1H),267.3(M+1H),285.2(M+1H),371.4(M+1H)and 415.2(M+1H),and the maximum proportion(5.63%)substance of all degradation products(126.0(M+1H))is 40-100 times less toxic than MTX.A mechanism for the removal and degradation of mitoxantrone was proposed.Besides,actual water experiments confirmed that the maximum removal capacity of MTX by g-C_(3)N_(4)@Fe/Pd is up to 492.4 mg/g(0.02 g/L,10 ppm).展开更多
Lake ecosystems,vital freshwater resources,are increasingly threatened by pollution from riverine inputs,making the management of these loads critical for preventing ecological degradation.Predicting the combined effe...Lake ecosystems,vital freshwater resources,are increasingly threatened by pollution from riverine inputs,making the management of these loads critical for preventing ecological degradation.Predicting the combined effects of multiple rivers on lake water quality is a significant challenge;traditional mechanistic models are computationally intensive and data-dependent,while conventional machine learning methods often fail to capture the system's multifaceted nature.This complexity creates a critical need for an integrated predictive tool for effective environmental management.Here we show a multi-task deep neural network(MTDNN)that can accurately and simultaneously predict four key water quality indicators—permanganate index,total phosphorus,total nitrogen,and algal density—at multiple locations within a complex lake system using data from its inflowing rivers.Our model,applied to Dianchi Lake in China,improves predictive precision by up to 56.3%compared to established mechanistic and single-task deep learning models.Furthermore,the model pinpoints the specific contributions of each river and identifies water temperature and wastewater effluent as dominant,sitespecific drivers of pollution.Scenario-based forecasting demonstrates that using reclaimed water for lake replenishment is a viable strategy that does not cause deterioration.This MTDNN framework offers a powerful and transferable tool for data-driven lake management,enabling targeted interventions and sustainable water resource protection.展开更多
基金Financial support from the National Natural Science Foundation of China (No.22176147)the National Science Fund for Excellent Young Scholars of China (No.21822607)+1 种基金the Fundamental Research Funds for Central Universities (No.22120230295)the State Key Laboratory for Pollution Control is acknowledged.
文摘The overuse of antibiotics and antitumor drugs has resulted in more and more extensive pollution of water bodies with organic drugs,causing detrimental ecological effects,which have attracted attention towards effective and sustainable methods for antibiotics and antitumor drug degradation.Here,the hybrid nanomaterial(g-C_(3)N_(4)@Fe/Pd)was synthesized and used to remove a kind of both an antibiotic and antitumor drug named mitoxantrone(MTX)with 92.0%removal efficiency,and the MTX removal capacity is 450 mg/g.After exposing to the hybrid material the MTX aqueous solution changed color from dark blue to lighter progressively,and LC-UV results of residual solutions showthat a newpeak at 3.0min(MTX:13.2min)after removal by g-C_(3)N_(4)@Fe/Pd appears,with the simultaneous detection of intermediate products indicating that g-C_(3)N_(4)@Fe/Pd indeed degrades MTX.Detailed mass spectrometric analysis suggests that the nuclear mass ratio decreased from 445.2(M+1H)to 126.0(M+1H),169.1(M+1H),239.2(M+1H),267.3(M+1H),285.2(M+1H),371.4(M+1H)and 415.2(M+1H),and the maximum proportion(5.63%)substance of all degradation products(126.0(M+1H))is 40-100 times less toxic than MTX.A mechanism for the removal and degradation of mitoxantrone was proposed.Besides,actual water experiments confirmed that the maximum removal capacity of MTX by g-C_(3)N_(4)@Fe/Pd is up to 492.4 mg/g(0.02 g/L,10 ppm).
基金supported by the National Natural Science Foundation of China(No.52293442,52221004,52470043)。
文摘Lake ecosystems,vital freshwater resources,are increasingly threatened by pollution from riverine inputs,making the management of these loads critical for preventing ecological degradation.Predicting the combined effects of multiple rivers on lake water quality is a significant challenge;traditional mechanistic models are computationally intensive and data-dependent,while conventional machine learning methods often fail to capture the system's multifaceted nature.This complexity creates a critical need for an integrated predictive tool for effective environmental management.Here we show a multi-task deep neural network(MTDNN)that can accurately and simultaneously predict four key water quality indicators—permanganate index,total phosphorus,total nitrogen,and algal density—at multiple locations within a complex lake system using data from its inflowing rivers.Our model,applied to Dianchi Lake in China,improves predictive precision by up to 56.3%compared to established mechanistic and single-task deep learning models.Furthermore,the model pinpoints the specific contributions of each river and identifies water temperature and wastewater effluent as dominant,sitespecific drivers of pollution.Scenario-based forecasting demonstrates that using reclaimed water for lake replenishment is a viable strategy that does not cause deterioration.This MTDNN framework offers a powerful and transferable tool for data-driven lake management,enabling targeted interventions and sustainable water resource protection.
