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。     

Ozonation of pentachlorophenol in unsaturated soils

A heterogeneous model was developed to describe interactions between ozone and hydrophobic organic compounds, exemplified by pentachlorophenol, in highly gas-saturated vadose zones where water moisture was limited to a thin film on soil particle surfaces. The soil was assumed to be free of soil organic matter. The model included a set of transient equations considering diffusion with simultaneous chemical reaction and hydrophobic partitioning. From dimensionless analysis, it was found that the film concentrations of ozone and the hydrophobic organic component were dependent on the Damk?hler numbers. Effects of Damk?hler numbers on the film profiles of components were examined. With the interfacial flux of ozone calculated from film profiles, dimensionless governing equations of ozone transport and contaminant removal across an experimental column were established. These equations were dependent on the Stanton number. One-dimensional column experiments were conducted to test the model. The optimal time for flow rate adjustment during the process was approximated. Finally, effects of ozone velocity and ozone gas concentration on the Stanton number were evaluated.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

The many benefits of employee well‐being in organizational research

We undertake three objectives in this Incubator. We begin with a discussion of how well‐being has been considered in organizational research. Second, we provide an overview of the relationship between employee well‐being and such workplace outcomes as job performance, employee retention, and cardiovascular health. Third, we introduce exciting research directions. Copyright © 2012 John Wiley & Sons, Ltd.