Microbial responses to combined oxidation and catalysis treatment of 1,4-dioxane and co-contaminants in groundwater and soil

Post-treatment impacts of a novel combined hydrogen peroxide (H₂O₂) oxidation and WO_x/ZrO₂ catalysis used for the removal of 1,4-dioxane and chlorinated volatile organic compound (CVOC) contaminants were investigated in soil and groundwater microbial community. This treatment train removed ~90% 1,4-dioxane regardless of initial concentrations of 1,4-dioxane and CVOCs. The Illumina Miseq platform and bioinformatics were used to study the changes to microbial community structure. This approach determined that dynamic shifts of microbiomes were associated with conditions specific to treatments as well as 1,4-dioxane and CVOCs mixtures. The biodiversity was observed to decrease only after oxidation under conditions that included high levels of 1,4-dioxane and CVOCs, but increased when 1,4-dioxane was present without CVOCs. WO_x/ZrO₂ catalysis reduced biodiversity across all conditions. Taxonomic classification demonstrated oxidative tolerance for members of the genera Massilia and Rhodococcus, while catalyst tolerance was observed for members of the genera Sphingomonas and Devosia. Linear discriminant analysis effect size was a useful statistical tool to highlight representative microbes, while the multidimensional analysis elucidated the separation of microbiomes under the low 1,4-dioxane-only condition from all other conditions containing CVOCs, as well as the differences of microbial population among original, post-oxidation, and post-catalysis states. The results of this study enhance our understanding of microbial community responses to a promising chemical treatment train, and the metagenomic analysis will help practitioners predict the microbial community status during the post-treatment period, which may have consequences for long-term management strategies that include additional biodegradation treatment or natural attenuation.

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A modified cyanobacteria prediction model based on cellular automata model using N and P concentration reverse data: a case study in Taihu Lake

The problem of algal bloom caused by eutrophication has attracted global attention. Many scholars have studied the problem associated with algae bloom, but few have carried out dynamic monitoring, instead focusing on the formation mechanism of cyanobacteria. For our study of the Taihu Lake in China, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat remote sensing image data from 2017 to establish a prediction model. First, we used MODIS data to retrieve the concentration of N, P, and chlorophyll a in water. Then, we applied the analytic hierarchy process (AHP) model to the inversion results to construct the diffusion potential index. Finally, we used C# to compile the cellular automata (CA) model. We found that the distribution of cyanobacteria predicted by our method was consistent with the algal bloom situation of Taihu Lake in 2017. The results showed that the method effectively predicts the dynamic transfer of cyanobacteria from outbreak to diffusion in a short period of time, which can help decision-makers monitor lake health.

     

A sorption kinetics model for arsenic adsorption to magnetite nanoparticles

Introduction  

Arsenic is a well known water contaminant that causes toxicological and carcinogenic effects. In this work magnetite nanoparticles were examined as possible arsenic sorbents. The objective of this work was to develop a sorption kinetics model, which could be used to predict the amount of arsenic adsorbed by magnetite nanoparticles in the presence of naturally occurring species using a first-order rate equation, modified to include adsorption, described by a Langmuir isotherm.     

环境与经济综合核算的系统动力学模型

环境与经济综合核算（绿色GDP核算）是最主要的评价经济增长真实状况的手段之一。然而，绿色GDP的核算在国内外却备受争议。其原因就在于核算方式是不完整的、静态的、事后核算和分项计算的，对转变经济增长方式的指导意义有限。为了克服绿色GDP核算中存在的问题，首次提出了系统动力学绿色GDP核算方法，充分发挥了系统动力学结构功能分析的特点，将复杂的绿色GDP核算内容，统一在一个动态的、预测型的、具有反馈特征的模型之中；为经济增长方式的调整提供了充分的信息和调节端口；此外，绿色GDP系统动力学核算模型还可以作为一个核算和政策模拟平台，通过不断完善、调整而长期使用。