检索结果-内蒙古大学图书馆

Assessment of occurrence, partitioning and ecological risk for 144 steroid hormones in Taihu Lake using UPLC-MS/MS with machine learning model

引用

Chemosphere 2024年 354卷 141598页

作者： Zhao, Li Wang, Chao Sun, Fuhong Liao, Haiqing Chang, Hong Jia, Xudong Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Sciences & Engineering Beijing Forestry University Beijing100083 China China CDC Key Laboratory of Environment and Human Health National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing100021 China State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China NHC Key Laboratory of Food Safety Risk Assessment China National Center for Food Safety Risk Assessment Beijing100021 China

Steroid hormones (SHs) have attracted mounting attention due to their endocrine-disrupting effects on humans and aquatic organisms. However, the lack of analytical methods and toxicity data for a large number of SHs has limited the effective management of SH contamination in the water-sediment systems. In this study, we developed a highly sensitive analytical method for the simultaneous quantification of 144 SHs to investigate their occurrence, spatial distribution and partitioning in the water and sediment in Taihu Lake. The results showed that the total concentrations of SHs in water and sediment were 366.88–998.23 ng/L (mean: 612.84 ng/L) and 17.46–150.20 ng/g (mean: 63.41 ng/g), respectively. The spatial distribution of SHs in Taihu Lake might be simultaneously influenced by the pollution sources, lake hydrodynamics, and sediment properties. The sediment-water partitioning result implied that 28 SHs were in dynamic equilibrium at the water-water interface. In addition, 22 and 12 SHs tended to spread to water and settle into sediment, respectively. To assess the ecological risk of all SHs, a robust random forest model (R2 = 0.801) was developed to predict the acute toxicity of SHs for which toxicity data were not available from publications. Risk assessment showed that SHs posed a high ecological risk throughout Taihu Lake, with the highest risk in the northwestern areas. Estrone, 17β-estradiol and 17α-ethynylestradiol were the dominant risk contributors and were therefore recommended as the priority SHs in Taihu Lake. This work provided a valuable dataset for Taihu Lake, which would help to provide guidance and suggestions for future studies and be useful for the government to develop the mitigation and management measures. © 2024 Elsevier Ltd

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Facile Synthesis of Cofe2o4@Bc Activated Peroxymonosulfate for P-Nitrochlorobenzene Degradation: Matrix Effect and Toxicity Evaluation

SSRN

引用

SSRN 2022年

作者： Zhi, Zejian Wu, Di Meng, Fanyue Yin, Ying Song, Bing Zhao, Yan Song, Min Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Jiangsu Nanjing210096 China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University Nanjing210023 China

p-Nitrochlorobenzene (p-NCB) is widely used in industry and poses a potential threat to the public health due to its persistence, carcinogenicity and mutagenicity. Herein, magnetic catalyst CoFe 2 O 4 @Biochar (CoFe 2 O 4 @BC) was synthesized by a facile sol-gel method, efficiently activating peroxymonosulfate (PMS) to degrade p-NCB. The synergistic effect of Fe and Co in well-dispersed CoFe 2 O 4 and the electron transfer promote the production of reactive oxygen species (ROS) (•OH, SO 4 •- and O 2 •- ), efficiently removing p-NCB enriched by CoFe 2 O 4 @BC. Under optimum conditions, the CoFe 2 O 4 @BC/PMS system could remove 89% of p-NCB from water, and the degradation efficiency could reach 80% in soil. Toxic chlorinated intermediates appeared during the degradation process and thus efficient dechlorination process can lower the toxicity of the reaction solution, which was also proved by the oxygen uptake inhibition experiment as well as zebrafish toxicity experiments. Furthermore, p-NCB degradation efficiency could be inhibited by Cl - , HCO 3 - , HPO 4 2- and humic acid (HA) through quenching effect or occupation of CoFe 2 O 4 @BC surface active sites while HPO 4 2- could also improve the efficiency by directly activating PMS. The CoFe 2 O 4 @BC/PMS system can be efficiently applied in the remediation of p-NCB pollution in water and soil. © 2022, The Authors. All rights reserved.

关键词： Toxicity

来源：评论

学校读者我要写书评

暂无评论

Data Imputation of Omi No2 by Combining Multi-source Data Through a 2-Step Machine Learning Method Over China, 2007-2020

SSRN

引用

SSRN 2022年

作者： Zhao, Wei Liu, Riyang Shao, Yanchuan Li, Weihan Bi, Jun Ma, Zongwei State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University Nanjing Jiangsu China Nanjing University of Information Science & Technology Nanjing Jiangsu China

The Ozone Monitoring Instrument (OMI) aboard NASA's EOS-Aura satellite provides tropospheric NO 2 vertical column densities (VCDs) with global spatial coverage (13km×24km) and a high temporal resolution (daily), which is widely used in ground-level NO 2 estimation. However, the extensive non-random data missing of OMI NO 2 caused by "row anomaly" failures and cloud reflections severely affected the data quality and hindered relevant research. In this study, we innovatively adopted a two-step machine learning method to impute missing OMI NO 2 across China. In the first step, we used tropospheric NO 2 from the Second Global Ozone Monitoring Experiment (GOME-2), another satellite data product with similar operating parameters to OMI, as predictors to establish the OMI-GOME-2 combination model, which increased the data volume by 15%-40% and achieved higher spatial coverage. In the second step, a 3day-randomforest (3d-RF) data imputation model was built to interpolate the data to the entire study area. The two-step model achieved robust performance with cross-validation R 2 of 0.77-0.85 and 0.73-0.78, respectively. Finally, the high-resolution (0.1° × 0.1°) tropospheric NO 2 dataset with full spatial coverage in China during 2007-2020 was obtained which is expected to contribute to subsequent ground-level concentration estimation and environmental health research. © 2022, The Authors. All rights reserved.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Evaluating and Predicting Groundwater Drought in Semi-Arid Plains by an Interpretable Machine Learning Model Optimized by the Sparrow Search Algorithm

SSRN

引用

SSRN 2024年

作者： Gan, Zhiyuan Xie, Xianjun Su, Chunli Ge, Weili Pan, Hongjie Yang, Liangping School of Environmental Studies China University of Geosciences Wuhan430074 China State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution Ministry of Ecology and Environment Wuhan430074 China Geological Survey Institute of Inner Mongolia Autonomous Region Huhhot011020 China

Groundwater drought, as an emerging type of drought, has garnered widespread attention. However, the challenge of directly monitoring variations in groundwater storage has impeded the identification and prediction of groundwater drought. This study gathered a total of 3,474 water level observations from the West Liao River Plain (WLRP) and developed eight machine learning models under two optimization algorithms, Grid Search CV (GSCV) and Sparrow Search Algorithm (SSA), for machine learning model training. The results show the XGBoost model demonstrated commendable performance under both optimization algorithms, with models optimized with the SSA performing better than those optimized with the GSCV. SSA-XGBoost exhibited values for area under the curve (AUC), accuracy, precision, recall, and F1 score that were higher by 2.59%, 3.17%, 2.30%, 4.49%, and 3.48%, respectively, relative to GSCV-XGBoost, indicating good predictive capability and comprehensive interpretability. SHapley Additive exPlanations (SHAP) analysis revealed that long timescale meteorological drought and anthropogenic activities are the two most important factors for prediction. In addition, the machine learning models were adept at clarifying the nonlinear relationship between meteorological drought and groundwater drought. The interpretable machine learning models can be used to effectively identify the features of groundwater droughts resulting from various anthropogenic activities and climate change. Compared with traditional groundwater drought models, the interactions between various feature variables of our model were also reflected by SHAP analyses. SSA-XGBoost model successfully unraveled the intricate interrelationships among feature variables, thereby improving the accuracy of predictions. Predictions indicated the WLRP will experience severe groundwater drought in the future, necessitating changes in irrigation patterns and rational management of groundwater resources. This study demonstr

关键词： Groundwater

来源：评论

学校读者我要写书评

暂无评论

Reply for comment on "Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow [Water Research 228 (2023), 119356]"

引用

Water Research 2023年 236卷 119928页

作者： Luo, Tianyi Wei, Wei Ni, Bing-Jie State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and Engineering Tongji University Shanghai 200092 China Centre for Technology in Water and Wastewater School of Civil and Environmental Engineering University of Technology Sydney SydneyNSW2007 Australia

来源：评论

学校读者我要写书评

暂无评论

Insight on prioritization of antibiotics in China, their occurrence, and removal by different wastewater treatment technologies

引用

Discover Environment 2024年第1期2卷 1-34页

作者： Bavumiragira, Jean Pierre Eheneden, Iyobosa Yin, Hailong Mumbi, Anne Wambui Quoie, Gerald D. S. Uyisaba, Pascaline Wang, Rongchang Zhao, Jianfu Institute of Environment for Sustainable Development College of Environmental Science and Engineering Tongji University Shanghai 200092 China Key Laboratory of Yangtze River Water Environment (MoE) Tongji University Shanghai 200092 China Institute of Biofilm Technology Key Laboratory of Yangtze Aquatic Environment (MOE) State Key Laboratory of Pollution Control and Resource Reuse Tongji University Shanghai 200092 China Harper Adams University Edgmond Newport TF10 8NB United Kingdom College of Medicine and Health Sciences University of Rwanda Kigali Rwanda

China still dominates all other nations as the world's most significant producer and consumer of antibiotics. Antibiotic misuse and overuse have been qualitatively and quantitatively documented in China recently. Antibiotic misuse has alarmed the Chinese population because some antibiotics persist in the environment and adversely affect human health and other non-target organisms. Antibiotic priority setting has been considered the best monitoring tool that is also affordable and time-efficient. Therefore, this article aims to assess the status methods of antibiotic prioritization within ten years ago years (2012–2022) in China and its prevalence and removal by conventional wastewater treatment facilities. Twenty-six priority antibiotics in China may need more attention, according to a recent prioritization assessment. According to other nations, the outcomes of prioritization vary from one nation to another. However, the same antibiotics are frequently reported despite applying various prioritization techniques (method). Their prevalence and frequent detection in China’s environmental media indicate that conventional treatment plants cannot remove them from effluents altogether. Their removal patterns vary from wastewater treatment plant (WWTP) to another and are affected by different factors, including pH, physical–chemical properties of the antibiotic compound, temperature, sludge retention time (SRT), hydraulic retention time (HRT), and the amount of microorganism present. © The Author(s) 2024.

关键词： Antibiotics Occurrence Prioritization Removal Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

A two-stage robust decision-making framework (2S-RDM) for flood risk adaptation under deep uncertainty

引用

Fundamental Research 2024年

作者： Cai, Jiacong Wei, Yiding Yang, Jianxun Ji, Chenyi Liu, Miaomiao Fang, Wen Ma, Zongwei Bi, Jun State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University Nanjing 210044 China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science & Technology Nanjing 210044 China

Flood is one of the major challenges facing human societies. Adapting to future flood risks involves deep uncertainty, especially when long-term projections of climate change are considered. This study proposed a Two-stage Robust Decision Making (2S-RDM) framework to help devise flexible and robust strategies capable of addressing the inherent deep uncertainty associated with managing flood risks. Taking the Yangtze River Basin in China as a case study, we simulated flood risks across ∼0.6 million scenarios until 2050. This analysis considered four types of uncertain factors, i.e., future climate change, socio-economic growth, industrial structure transformation, and population aging. We then examined the effectiveness of four adaptation measures and their combinations, i.e. building elevation, tunnel construction, people relocation, and river basin conservation. Our projections show that without immediate adaptation, an estimated 0.9 to 27.3 million people will be impacted by floods until 2050, accompanied with $33.8 to $198.5 billion economic losses in the entire basin. When defining the goal as limiting the affected population <0.05% and ensuring economic losses <0.02%, we identified 24 global robust strategies capable of meeting this criterion in >80% of scenarios. Then, we compared the 24 global robust strategies regarding their relative costs and performances in each of the future scenario pools. The final recommended solutions are hybrid strategies that integrate engineering-based measures with ‘soft’ adaptation options (e.g. Elevation++, Tunnel++, and Relocation). This study provides tools to design flood adaptation strategies not only robust across diverse scenarios but also flexible for decision-makers to customize and refine their strategies based on specific needs. © 2024

关键词： Deep uncertainty Flood risk adaptation Robust decision-making Scenario analysis

来源：评论

学校读者我要写书评

暂无评论

Leaching behavior of phosphorous compounds from sewage sludge ash based on quantitative X-ray diffraction analysis

引用

Waste Disposal and Sustainable Energy 2020年第2期2卷 113-125页

作者： Pinjing He Xiaotong Zhang Fan Lü Liming Shao Hua Zhang Institute of Waste Treatment and Reclamation College of Environmental Science and EngineeringTongji UniversityShanghai 200092China State Key Laboratory of Pollution Control and Resource Reuse Tongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Phosphorus(P)is an essential and limited *** sewage sludge ash(ISSA)contains a high amount of P,which can be recovered using chemical leaching ***,the recovery ratio depends on the speciation of P and the leaching *** this study,hydrochloric acid was used as a leaching agent,and the effects of the hydrochloric acid concentration,leaching time,temperature,and liquid-solid ratio on the P leaching ratio were ***,the co-leaching of macro-metals Ca,Al,Fe,and Mg was *** results showed that P leached rapidly within 30 min,where the leaching rate reached more than 80%and then gradually *** leaching concentrations of Ca and Mg had a significant correlation(correlation coefficient r^(2)>0.90),and both were leached *** and P had similar leaching patterns,where the leaching rates increased initially and then decreased with time at 0.2 mol/L *** to X-ray diffraction analysis and Rietveld refinement,the P in ISSA was mainly present in the forms of Mg_(3)Ca_(3)(PO_(4))_(4) and AlPO_(4).When leached using 0.2 mol/L HCl at 55℃ with a liquid-to-solid ratio of 20 L/kg,the AlPO_(4) and Fe_(3)(H_(2)O)_(3)(PO_(4))_(2) in ISSA dissolved initially and then precipitated on the surface of the solid phase,thereby impeding further P leaching.

关键词： Sewage sludge ash Acid leaching Phosphorous speciation Quantitative X-ray diffraction analysis Rietveld refinement

来源：评论

学校读者我要写书评

暂无评论

Improving the Efficiency of Machine Learning in Predicting Sediment Heavy Metal Contamination by Adding Feature Selection Methods

SSRN

引用

SSRN 2022年

作者： Deng, Ligang Gao, Xiang Xia, Bisheng Wang, Jinhua Dai, Qianying Fan, Yifan Wang, Siyuan Li, Huiming Qian, Xin State Key Laboratory of Pollution Control and Resource Reuse School of Environment Nanjing University Nanjing210023 China School of Environment Nanjing Normal University Nanjing210023 China College of Mathematics and Computer Science Yan'an University Yan'An716000 China Key Laboratory of Water Pollution Control and Wastewater Reuse of Anhui Province Anhui Jianzhu University Hefei230009 China

Sediment cores were collected in Taihu Lake and analyzed by radionuclide dating to determine their chronology. Heavy metals and magnetic properties of each core slice were assessed. The concentrations of most heavy metals in Taihu Lake surged at 20 cm from the surface, along with the concentrations of single-domain magnetic particles, consistent with an influence of anthropic activities on the lake’s environment after the 1990s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with a radial basis function support vector machine (RF-SVM) model to construct heavy metal simulation models, with the inclusion of selected key magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC for Cd, Cr, Cu, Pb and Sb when fewer parameters were included. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), their correlation coefficients for observed and predicted data were 0.73 to 0.97 when only 14–27% of the parameters selected by RF were used as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments. © 2022, The Authors. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Hydrogeochemical Analysis and Paleo-Hydrogeological Modeling of Shallow Groundwater Salinization Processes in North China Plain

SSRN

引用

SSRN 2024年

作者： Huang, Yihong Yang, Jie Yu, Xuan Wang, Shijie Xie, Xianjun Li, Junxia Wang, Yanxin MOE Key Laboratory of Groundwater Quality and Health China University of Geosciences Wuhan430078 China State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution School of Environmental Studies China University of Geosciences Wuhan430078 China Key Laboratory of Resources and Eco-environmental Geology Geological Experimental Testing Center of Hubei Province Wuhan430034 China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing210008 China

Coastal groundwater salinization induced by marine transgression, sea level rising, and/or seawater intrusion due to anthropogenic activities has been a grand challenge for water security worldwide. Protecting the invaluable fresh groundwater resources from salinization necessitates the understanding of the spatial distribution of saline water and its sources. In this study, the process of groundwater salinization in two shallow aquifers of the North China Plain near Bohai Sea was characterized using hydrogeochemical analysis and paleo-hydrogeological modeling. The spatial variation in groundwater chemistry and isotopic composition in the two aquifers reflected the evident influence of paleo-transgression/regression events, while paleo-hydrogeological modeling further confirmed the primary role of several events of marine transgressions in groundwater salinization. Moreover, the fingering process of vertical saline water infiltration by free convection was identified from the results of the modeling, which was constrained by preferential flow paths and low permeability barriers of aquitards. The modeling results show that seawater infiltration was rapid, reaching a depth of 120 m B.S.L. in a short time at an average rate of 23 mm/yr. The trend of downward permeation of shallow saline water may threaten the groundwater quality of deep confined aquifers that sever as the main sources of water supply in coastal area of the North China Plain. © 2024, The Authors. All rights reserved.

关键词： Groundwater resources

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：