Similarity quantification of soil parametric data and sites using confidence ellipses

This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data using the database from the site investigation reports.Then,the obtained similarity assessment results of parametric data are used to further estimate the site similarity via two proposed strategies,namely the mean and weighted mean approaches.The former referred to the average of parametric data similarity degrees,while the latter was the weighted average,and the weight was calculated using the coefficient of variation(COV)of each parameter.For illustration,the liquidity index(LI)dataset was firstly used to explore the performance of the presented method in the evaluation of parametric data similarity.Subsequently,the site similarity was assessed and the effects of numbers and weights of selected parameters for study were systematically studied.Lastly,the transformation models about the relationships between Cc and x as well as between Cc and e0 were constructed to illustrate the application of the similarity analysis in reduction of transformation uncertainty.Results show that the greatest site similarity degree is at about 0.76 in this study,and the maximum decrease of transformation uncertainty can reach up to 18%and 25.5%as union parametric data similarity degree increases.Moreover,the site similarity degree represents the whole similarity between two different sites,and the presented union parameter similarity degree maintains a good agreement with transformation uncertainty.

Yangtze Three Gorges Reservoir, China: A holistic assessment of organic pollution, mutagenic effects of sediments and genotoxic impacts on fish

Besides obvious benefits,the Three Gorges Dam＇s construction resulted in new pollution scenarios with the potentials to threaten the Three Gorges Reservoir（TGR） ecosystem.In order to record organic contamination,to find links to ecotoxicological impacts and to serve as reference for ensuing monitoring,several sites in the TGR area were screened applying the triad approach with additional lines-of-evidence as a holistic assessment method.Sediments and the benthic fish species Pelteobagrus vachellii were sampled in 2011 and 2012 to determine organic pollution levels,mutagenic potentials and genotoxic impacts.Two regional hot-spots near the cities of Chongqing and Kaixian were identified and further investigated in 2013.Only polycyclic aromatic hydrocarbons（PAHs） could be detected in sediments in 2011（165-1653 ng/g）,emphasizing their roles as key pollutants of the area.Their ubiquity was confirmed at Chongqing（150-433 ng/g） and Kaixian（127-590 ng/g） in2013.Concentrations were comparable to other major Chinese and German rivers.However,the immense sediment influx suggested a deposition of 216-636 kg PAH/day（0.2-0.6 mg PAH/（m2·day））,indicating an ecotoxicological risk.PAH source analysis highlighted primary impacts of combustion sources on the more industrialized upper TGR section,whereas petrogenic sources dominated the mid-low section.Furthermore,sediment extracts from several sites exhibited significant activities of frameshift promutagens in the Ames fluctuation assay.Additionally,significant genotoxic impairments in erythrocytes of P.vachellii were detected（Chongqing/Kaixian）,demonstrating the relevance of genotoxicity as animportant mode of action in the TGR＇s fish.PAHs,their derivatives and non-target compounds are considered as main causative agents.

应对环境变化的多功能湿地设计——三峡库区汉丰湖芙蓉坝湖湾湿地生态系统建设

汉丰湖是一处因三峡水库蓄水造成的季节性水位消涨而形成的城市内湖,面临着水环境污染、水生生物物种消失或濒危、土地利用格局改变等问题。本文以汉丰湖芙蓉坝湖湾湿地生态系统建设为例,探讨如何借鉴中国传统农耕时代与治水、利水、善水相关的生态智慧,运用动态景观设计、多功能景观设计、环湖圈层设计和协同进化及互利共生等理念,打造能够适应环境变化的多功能消落带湿地。

Quantifying the spatiotemporal characteristics of ecosystem services and livelihoods in China’s poverty-stricken counties

Understanding the coupling relationship between ecosystem services and poverty livelihoods is a prerequisite for poverty eradication and ecological protection in China.In this paper,we intended to quantify the coupling relationship between ecosystem services and poverty livelihoods in 717 poverty-stricken counties in China.First,we identified distinct levels of ecosystem services and poverty respectively in the poverty-stricken areas based on the ecosystem services subsystem index and poverty livelihoods subsystem index.Then,we calculated the coupling degree and coupling coordination degree between the ecosystem services and the poverty livelihoods in China^poverty-stricken counties from 2000 to 2015.Finally,we analyzed the temporal and spatial evolution of the coupling between ecosystem services and poverty livelihoods.The results indicate that the ecosystem services showed a trend of first decreasing and then increasing,while the poverty level showed an increased trend.In addition,the coupling relationship between ecosystem services and poverty livelihoods in poverty-stricken counties was also improved from 2000 to 2015.However,the coupling degree value or coupling coordination degree values in some counties or contiguous impoverished areas showed a decreased trend,the coupling relationship between ecosystem services and poverty livelihoods in those counties or areas was higher conflict.Future more,the coupling relationship in China's poverty-stricken counties was mainly intermediate conflict and low conflict,which need to improve in the future.Thus,it is suggested that effective measures are required to lift ecosystem services out of the poverty trap and into sustainable livelihoods.

Zeolite-based Fenton-like catalysis for pollutant removal and reclamation from wastewater

Wastewater treatment and reclamation from wastewater are essential for the sustainable use of water resource.Zeolite-based heterogeneous catalysis shows great potential in circumventing the current limitations on pollutant removal and transformation to useful chemicals,inspiring advancements towards practical water treatment.This paper summarizes the methods for synthesizing zeolite-based catalyst,and the corresponding advantages and disadvantages.In comparison with traditional Fenton-like reaction,the superiority of zeolite-based catalysis lies in less sludge,wide pH range and easy recyclability.Accordingly,applications of zeolite-based Fenton-like catalysis(ZFCs)in pollutant removal and reclamation of wastewater were reviewed.Emphasis was placed on the methodological strategies in improving ZFCs,including the combination of external driving force(e.g.,photocatalysis or electrochemistry),as well as the introduction of various transition metals into zeolite-based catalyst.Possible challenges and future perspectives for ZFCs were proposed.

Machine learning predicting and engineering the yield,N content,and specific surface area of biochar derived from pyrolysis of biomass

Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas,such as removal of pollutants,adsorption of CO_(2)and H2,catalysis,and energy storage.Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials.This study attempted to build machine learning models to predict and optimize specific surface area of biochar(SSA-char),N content of biochar(N-char),and yield of biochar(Yield-char)individually or simultaneously,by using elemental,proximate,and biochemical compositions of biomass and pyrolysis conditions as input variables.The predictions of Yield-char,N-char,and SSA-char were compared by using random forest(RF)and gradient boosting regression(GBR)models.GBR outperformed RF for most predictions.When input parameters included elemental and proximate compositions as well as pyrolysis conditions,the test R^(2) values for the single-target and multi-target GBR models were 0.90-0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R^(2) of 0.84 and the three-target prediction model which had a test R^(2) of 0.81.As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models,the top influencing factors toward predicting three targets were specified as follows:pyrolysis temperature,residence time,and fixed carbon for Yield-char;N and ash for N-char;ash and pyrolysis temperature for SSA-char.The effects of these parameters on three targets were different,but the trade-offs of these three were balanced during multi-target ML prediction and optimization.The optimum solutions were then experimentally verified,which opens a new way for designing smart biochar with target properties and oriented application potential.