Effects of artificially induced complete mixing on dissolved organic matter in a stratified source water reservoir

Naturally complete mixing promotes the spontaneous redistribution of dissolved oxygen(DO), representing an ideal state for maintaining good water quality, and conducive to the biomineralization of organic matter. Water lifting aerators(WLAs) can extend the periods of complete mixing and increase the initial mixing temperature. To evaluate the influence of artificial-induced continuously mixing on dissolved organic matter(DOM) removal performance, the variations of DOM concentrations, optical cha...     

Predicting geogenic groundwater fluoride contamination throughout China

Endemic fluorosis exists in almost all provinces of China. The long-term ingestion of groundwater containing high concentrations of fluoride is one of the main causes of fluorosis. We used artificial neural network to model the relationship between groundwater fluoride concentrations from throughout China and environmental variables such as climatic, geological. and soil parameters as proxy predictors. The results show that the accuracy and area under the receiver operating characteristic curve of the model in the test dataset are 80.5% and 0.86%, respectively, and climatic variables are the most effective predictors. Based on the artificial neural network model, a nationwide prediction risk map of fluoride concentrations exceeding 1.5 mg/L with a 0.5 × 0.5 arc minutes resolution was generated. The high risk areas are mainly located in western provinces of Xinjiang, Tibet, Qinghai, and Sichuan, and the northern provinces of Inner Mongolia, Hebei and Shandong. The total number of people estimated to be potentially at risk of fluorosis due to the use of untreated high fluoride groundwater as drinking water is about 89 million, or 6% of the population. The high fluoride groundwater risk map helps the authorities to prioritize areas requiring mitigation measures and thus facilitates the implementation of water improvement and defluoridation projects.     

Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites

Summary. Individuals in an insect colony need to identify one another according to caste. Nothing is known about the sensory process allowing nestmates to discriminate minute variations in the cuticular hydrocarbon mixture. The purpose of this study was to attempt to model caste odors discrimination in four species of Reticulitermes termites for the first time by a non-linear mathematical approach using an "artificial neural network" (ANN). Several rounds of testing were carried out using 1 – the whole hydrocarbon mixtures 2 – mixtures containing the hydrocarbons selected by principal component analysis (PCA) as the most implicated in caste discrimination. Discrimination between worker and soldier castes was tested in all four species. For two species we tested discrimination of four castes (workers, soldiers, nymphs, neotenics). To test cuticular pattern similarity in two sibling species (R. santonensis and R. flavipes), we performed two experiments using one species for training and the other for query. Using whole hydrocarbons mixtures, worker/soldier discrimination was always successful in all species. Network performance decreased with the number of hydrocarbons used as inputs. Four-caste discrimination was less successful. In the experiment with the sibling species, the ANN was able to distinguish soldiers but not workers. The results of this study suggest that non-linear mathematical analysis is a good tool for classification of castes based on cuticular hydrocarbon mixture. In addition this study confirms that hydrocarbon mixtures observed are real chemical entities and constitute a true chemical signature or odor. Whole mixtures are not always necessary for discrimination. Received 23 July 1998; accepted 9 October 1998.     

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.     

对人工湿地污水处理系统工艺设计技术关键的探讨

重点探讨了人工湿地污水处理系统工艺设计过程中的技术关键问题,提出系统水力停留时间、水传导、表面负荷率、系统深度、处理单元长宽及其比例、进出水系统布置、防渗材料等七点是人工湿地污水处理系统设计的技术关键。     

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

The catastrophic consequences of recent NaTech events triggered by earthquakes highlighted the inadequacy of standard approaches to seismic risk assessment of chemical process plants. To date, the risk assessment of such facilities mainly relies on historical data and focuses on uncoupled process components. As a consequence, the dynamic interaction between process equipment is neglected. In response to this gap, researchers started a progressive integration of the Pacific Earthquake Engineering Research Center (PEER) Performance-Based Earthquake Engineering (PBEE) risk assessment framework. However, a few limitations still prevent a systematic implementation of this framework to chemical process plants. The most significant are: (i) the computational cost of system-level simulations accounting for coupling between process equipment; (ii) the experimental cost for component-level model validation; (iii) a reduced number of hazard-consistent site-specific ground motion records for time history analyses.In response to these challenges, this paper proposes a recently developed uncertainty quantification-based framework to perform seismic fragility assessments of chemical process plants. The framework employs three key elements: (i) a stochastic ground-motion model to supplement scarcity of real records; (ii) surrogate modeling to reduce the computational cost of system-level simulations; (iii) a component-level model validation based on cost-effective hybrid simulation tests. In order to demonstrate the potential of the framework, two fragility functions are computed for a pipe elbow of a coupled tank-piping system.     

人工神经网络和专家系统在污水生物处理系统中的应用

对近年来国内外污水生物处理系统中人工神经网络和专家系统的应用进行了简要的回顾。分析了废水生物处理工艺难于控制的原因及人工神经网络和专家系统的结构和特点。结果表明．国外智能控制发展迅速,并且应用领域遍及污水生物处理的各个方面,国内尚处于起步阶段。简要探讨了废水生物处理智能控制今后应深入研究的问题及方向。     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Interspecific mating of the introduced bumblebee <Emphasis Type="Italic">Bombus terrestris</Emphasis> and the native Japanese bumblebee <Emphasis Type="Italic">Bombus hypocrita sapporoensis</Emphasis> results in inviable hybrids

The bumblebee Bombus terrestris is not only an effective pollinator, but also a potential invasive alien species outside its native range. Recently, nearly 30% of queens of the Japanese native species Bombus hypocrita sapporoensis and B. hypocrita hypocrita were estimated to copulate with B. terrestris males in the field, suggesting that indigenous bumblebees could be genetically deteriorated through hybrid production with the introduced species. In this study, we evaluated hybrid production between the introduced B. terrestris and the indigenous B. hypocrita sapporoensis under laboratory conditions. The hatching rate of eggs derived from interspecific matings was 0% and 8.6% depending on the direction of the cross, which was significantly lower than that from intraspecific matings of B. terrestris (76.9%) and B. hypocrita sapporoensis (78.9%). Genetic studies using microsatellite markers revealed that both haploid and diploid individuals were present in the egg stage, whereas all hatched larvae were haploid. In addition, histological studies revealed that eggs derived from interspecific matings terminated development 2 days after oviposition. These results strongly suggested that eggs derived from interspecific matings are inviable due to post-mating isolation mechanisms. Mass release of exotic pollinators could cause serious population declines of native bumblebee species.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.