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.     

Removal of SO2 from smelting flue gas by using copper tailings with MnSO4: factors optimization by response surface methodology

Environmental Science and Pollution Research - The abatement of SO2 and the utilization of copper tailings are identified as two attention-attracting environmental issues in the copper smelter. In...     

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.

     

北京和上海典型复合包装废物处置环境影响比较

采用生命周期评价法研究比较了北京和上海两地纸塑铝复合包装处置阶段的环境影响。通过现场和资料调研的方式获得此阶段的能量物质的输入输出和环境外排数据。结果表明:北京和上海两地纸塑铝复合包装处置阶段的环境影响潜值分别为-0.428 Pt和9.776 Pt,其主要集中在气候变化、土地占用和无机物对健康的损害三方面;每提高10%的回收率,其环境影响潜值北京和上海可分别降低5.446 Pt和5.799 Pt;上海地区纸塑铝复合包装处置阶段对环境的影响在任何同回收率的情况都要高于北京地区,其主要原因是上海地区填埋产生的温室气体释放量过大和再生企业距离打包点较远。     

小型燃油锅炉NOx的排放特征与管理控制

以68台燃油锅炉（≤10～MW）NOx排放实测数据为基础,通过统计分析方法,研究了NOx的排放特征;通过对比分析,探讨了我国燃油锅炉NOx排放控制与管理现状,讨论了进一步加强我国燃油锅炉NOx排放管理控制的可能性与可行性,并提出了相应的管理控制建议。结果表明,NOx平均排放浓度为318．2mg／m^3,基于燃料消耗量的平均排放因子为4．4kg／t,基于燃料发热量的平均排放因子为102．8ng／J,基于燃料氮含量的平均排放因子为2．1mg／mg;建议采取分阶段控制的方式,逐步提高NOx排放限制,从而实现控源减排目标。     

水解酸化-A2O污泥减量工艺的运行性能研究

生物处理单元采用水解酸化、多级串联接触曝气、连续流的除磷脱氮A2／O工艺,并辅以外排厌氧富磷污水侧流除磷,开发了一个新型的具有强化除磷脱氮功能的污泥减量HA—A／A—MCO工艺。用该工艺处理校园生活污水发现,在SRT60d、进水COD316～407mg／L、NH4＋-N30～40mg／L、TN35～53mg／L、TP8—12mg／L的条件下,出水COD≤18mg／L、NH4＋-N≤2．1mg／L、TN≤10．3mg／L、TP≤0．44mg／L。研究还发现,水解酸化池处理产生的VFA能有效促进生物除磷脱氮,导致厌氧释磷量达57mg／L,进入化学除磷池的侧流液量仅相当于进水量的13％;系统最主要的脱氮形式是SND和缺氧反硝化,SND脱氮占脱氮总量的50％,缺氧反硝化占26％;HA-A／A—MCO系统有效实现了生物相分离,并利用生物捕食作用获得较低的污泥产率,0．1gMLSS／gCOD。     

中小型工业窑炉NOx废气的净化技术与应用

介绍了中小型工业窑炉生产过程中排放NOx的现状,并针对NOx的治理问题,从工程应用角度,简述了湿法和干法净化NOx的原理和化学反应过程。讨论了NOx排放特性和NOx废气中NO2/NOx比例的问题。提出了影响NOx治理的主要因素,包括吸收剂、吸附剂、催化剂和设备的部分应用参数,并结合工程实例指出工程需要注意的问题。介绍了多项采用湿法和干法净化NOx的工程应用项目,丰富了中小工业窑炉NOx废气的治理的实践经验,为中小工业窑炉排放NOx治理奠定了基础。     

Impact of roadside noise barriers on particle size distributions and pollutants concentrations near freeways

Increasing epidemiological evidence has established an association between a host of adverse health effects and exposure to ambient particulate matter (PM) and co-pollutants, especially those emitted from motor vehicles. Although PM and their co-pollutants dispersion profiles near the open freeway have been extensively characterized by means of both experimental measurements and numerical simulations in recent years, such investigations near freeways with roadside barriers have not been well documented in the literature. A few previous studies suggested that the presence of roadside structures, such as noise barriers and vegetation, may impact the decay of pollutant concentrations downwind of the freeway by limiting the initial dispersion of traffic emissions and increasing their vertical mixing due to the upward deflection of airflow. Since the noise barriers are now common roadside features of the freeways, particularly those running through populated urban areas, it is pertinent to investigate the impact of their presence on the particles and co-pollutants concentrations in areas adjacent to busy roadways. This study investigated two highly trafficked freeways (I-710 and I-5) in Southern California, with two sampling sites for each freeway, one with and the other without the roadside noise barriers. Particle size distributions and co-pollutants concentrations were measured in the immediate proximity of freeways and at different distances downwind of the freeways. The results showed the formation of a “concentration deficit” zone in the immediate vicinity of the freeway with the presence of roadside noise barrier, followed by a surge of pollutant concentrations further downwind at 80–100 m away from freeway. The particle and co-pollutants concentrations reach background levels at farther distances of 250–400 m compared to 150–200 m at the sites without roadside noise barriers.