Exploring the embodied carbon flow interactive relationships in China from an ecological network perspective: a model framework and application at provincial level

Environmental Science and Pollution Research - Energy-related carbon emissions take a large proportion in China, and the interregional trade caused by provincial disparities has led to significant...     

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

Environmental Science and Pollution Research - To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed...     

TiO2 photocatalytic degradation of PCBs in soil-water systems containing fluoro surfactant

Titanium dioxide-mediated photodegradation of Polychlorinated biphenyls (PCBs) in soil/aqueous systems with added fluorinated surfactant was investigated. PCBs can bind tightly to organic matter in the soil, especially in aged, contaminated soil. Experiments showed an effective PCB photocatalytic degradation in mixed systems of soil/clay with anionic fluorinated surfactant FC-143 and TiO2. The FC-143 surfactant is stable in this photochemical process. PCB degradation rates in samples followed the order: spiked clay > spiked soil > Hudson River bank soil. The results suggest that anionic fluorinated surfactant may form semimicelles and/or admicelles on the surface of positively charged TiO2. The hydrophobic surface of TiO2 can provide a nonpolar phase that acts as a partioning medium for hydrophobic PCBs. Therefore, PCBs in soil can be released to the semimicelle and/or admicelle on the TiO2 surface and are effectively photodegraded in a dispersion containing anionic fluorinated surfactant. The combination of surfactant extraction and photooxidation forms the basis for a novel two-stage process for the removal and destruction of PCBs from soil.     

Biodegradation of 2,4,6-trichlorophenol in the presence of primary substrate by immobilized pure culture bacteria

In this study, pure strains that are capable of utilizing 2,4,6-trichlorophenol have been isolated from the mixed culture grown on substrates containing chlorophenolic compounds. Studies have been carried out on the capability of these isolated pure strains in suspended and immobilized forms to decompose 2,4,6-trichlorophenol. Additionally, the influence of primary substrates (e.g., phenol, 2-chlorophenol, 3-chlorophenol, 4-chlorophenol, 2,4-dichlorophenol) on the decomposition of 2,4,6-trichlorophenol by the isolated pure strains grown in immobilized form is also investigated. The results are: Through bacterial isolation and identification, three pure strains have been obtained: Pseudomonas spp. strain 01, Pseudomonas spp. strain 02 and Agrobacterium spp. Whether in suspended or immobilized forms, all strains have poor removal efficiencies of 2,4,6-trichlorophenol. However, addition of 200 mg/l phenol will enable the immobilized Pseudomonas spp. strain 01, and Pseudomonas spp. strain 02 to achieve 65% and 48% removal of 2,4,6-trichlorophenol, respectively. Addition of phenol will assist the immobilized Pseudomonas spp. strain 02 in achieving removal of 2,4,6-trichlorophenol but the removal efficiency is not good if the phenol concentration is too low. The optimum phenol concentration should be between 200 and 400 mg/l.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.     

Effects of green spaces on alleviating mortality attributable to PM2.5 in China

Environmental Science and Pollution Research - Increasing research suggested that green spaces are associated with many health benefits, but evidence for the quantitative relationship between green...     

Assessing economic sustainability and ecological efficiency with genuine progress indicator: a case study of the Yangtze River Delta from 2000 to 2018

To achieve urban sustainability, it is critical to enhance the environment, economy, and society simultaneously. This study adopted the revised genuine progress indicator (GPI) and ecological footprint (EF) to evaluate the ecological efficiency and economic sustainability of the Yangtze River Delta from 2000 to 2018. Spatial analysis was utilized to identify spatial autocorrelation. A total of 27 cities were then partitioned through k-means cluster analysis. The results showed that GPI and ecological efficiency improved rapidly, but economic sustainability showed a downward trend. GPI and GDP had a high degree of spatial correlation, especially in Suzhou-Wuxi-Changzhou Metropolitan Area. However, no spatial correlation existed between GPI and EF. The city with high GEE can reach 3000 $/gha, indicating the city consumed 1 global hectare to create $3000 of genuine economic growth. Shanghai, Hangzhou, and Taizhou were cities with the highest level of economic sustainability and ecological efficiency. The spatiotemporal characteristics of economic sustainability and ecological efficiency revealed in this study will provide theoretical guidance for alleviating ecological pressure and promoting economic sustainable development.

     

活性污泥系统优化设计对不确定性参数的灵敏度研究

在活性污泥系统优化设计模型参数不确定性分析的基础上,考察了进水负荷、生物动力学参数、曝气设备性能参数和二沉池浓缩常数变化对优化设计的影响。灵敏度分析结果表明,对优化设计影响较大的参数为：进水水量水质、与氧转移效率相关的校正参数以及二沉池的浓缩常数等。研究结果对于低灵敏度系统的设计,提高优化设计结果的工程实用性具有重要的意义。     

有机固体废物堆肥化处理的微生物学机理研究

由于符合持续发展的理念 ,利用微生物技术处理有机固体废物越来越受到人们的重视 ,其核心问题则是木质纤维素的生物降解。随着园林废物等高木质纤维素含量的城市生活垃圾的不断增加 ,以及对农业固体废物和食品工业废物再利用的需要 ,这一领域的研究取得了很大的进展。在研究中 ,针对好氧堆肥和厌氧发酵 2项主要的微生物处理技术 ,对其优势微生物菌群、不同降解底物和微生物降解动力学方面的最新研究进展进行了回顾和总结 ,并对环境微生物制剂的应用以及有机固体废物的微生物处理技术的发展做了合理的展望。     

城市生活污水厌氧生物处理发展现状

回顾了国内外污水厌氧生物处理工艺的发展情况,着重分析了中国城市污水的特点以及厌氧生物处理工艺在城市生活污水处理中的应用情况。