SBR法处理印染废水的研究

应用ＳＢＲ法、ＰＡＣ－ＳＢＲ法，微电解－ＳＢＲ法对印染废水进行了对比处理试验研究。试验结果表明：微电解－ＳＢＲ法处理效果优于其它两种方法，当进水ＣＯＤ＝１０００～１６００ｍｇ／Ｌ，色度＝２００～８００倍，ＢＯＤ５＝２５０～４００ｍｇ／Ｌ时．ＣＯＤ去除率在８５％左右，ＢＯＤ５去除率和脱色率均在９０％以上，出水达到排放标准。     

DHDPS的细胞表达、提纯和结晶

ＤＨＤＰＳ是赖氢酸合成通路中第一步的合成酶。将中国春小麦的ＤＨＤＰＳ基因质粒移植入ＲＤＡ８中，得到ＲＤＡ８／ｐＤＢ２６菌株，本研究通过细胞培养、层析提纯和结晶条件的探索，给出了一个较好的技术路线，并为开展义衍射分析蛋白结构创造了条件，通过该研究，对中国春小麦ＤＨＤＰＳ和野种大肠杆菌的ＤＨＤＰＳ差异有了一定的了解，并且对细胞培养的ＭＭ介质处理，ＤＥＡＥ－Ｓｅｐｈａｒｏｓｅ，Ｐｈｅｎｙｌ－Ｓｅｐｈａｒｏｓｅ和ＭｏｎｏＱ层析的方法给出具体实验条仲。酶活的检测和蛋白浓度测定都是采取高灵敏度的方法。结晶的ＳＣｒｅｅｎｉｎｇ条件对于二种ＤＨＤＰＳ有很大的差异。对野种大肠杆茵的ＤＨＤＰＳ，需要表面活性剂Ｎ－ｏｃｔｙｌ－Ｄ－ｇｌｕｃｏｐｙｒａｎｏｓｉｄｅ，ｐＨ１０．０～１０．５，对于中国春小麦一ＤＨＤＰＳ则未找到较好的表面活性剂，ｐＨ６．８～７．６。中国春小麦一ＤＨＤＰＳ晶体培养条件在以往文献中未见报导。     

荧光光纤传感器测定废水中邻硝基苯酚

将２－（４－二苯基）－６－苯基苯并恶唑包埋在增塑的ＰＶＣ膜中,基于邻硝基苯酚对ＰＢＢＯ荧光的猝灭,研制了一种邻硝基苯酚荧光光纤传感器。该传感器对邻硝基苯酚的响应具有良好的可逆性和重现性,响应时间小于５０ｓ,在１．２＊１０＾－４－２．０＊１０－６ｍｏｌ／Ｌ邻硝基苯酚浓度范围内具有好的线性关系。环境水中可能存在的常见阳离子、阴离子、酚、硝基化合物对邻硝基苯酚的测定不产生干扰,将传感器用于废水中的邻硝基     

高分子絮凝剂用于染色废水处理研究

本课题以染色废水中占比例最大的印染废水研究对象，用合成的含有多种活性基团的聚两性电解质ＰＡＮ－ＤＣＤ和ＰＡＮ－ＤＣＤ－ＨＹＡ以及阳离子聚电解质ＦＡ－２＾＃对印染厂废水进行脱色和去除ＣＯＤ效果评价，试验结果证明，这几种高分子絮凝剂对印染废水都优良的脱色性能，并与常用的无机絮凝剂作了比较。     

絮凝—酸化法预处理腈纶纺丝生产废水的实验研究

研究采用絮凝－酸化法对腈纶纺丝废水进行预处理,考察了絮凝剂的选用及多种絮凝实验条件对实验结果的影响。结果表明,以ＰＦＳ作絮凝剂,絮凝ｐＨ６．０,ＰＦＳ 投加量为０．３ｍｌ,ＰＡＭ投加量为０．４ｍｌ时的ＣＯＤ,ＢＯＤ５和ＮＨ３－Ｎ及ＣＮ＾－去除率分别为３０％,３１％,８％及３２％。     

M（∧,∨）型模糊综合评价法误判原因的探讨

Ｍ（∧，∨）型模糊综合评价法容易造成误判，现从数学上探讨误判的根本。认为此法不能客观，真实地反映环境质量状况，不宜用于环境质量评价。     

邻苯二甲酸酯类化合物土壤吸附系数的测定及相关性研究

研究测定了邻苯二甲酸二甲酯（ＤＭＰ）、二乙酯（ＤＥＰ）、二丙酯（ＤＰＰ）、二丁酯（ＤＢＰ）、丁基苄基酯（ＢＢＰ）和二异辛酯（ＤＥＨＰ）等６种化合物土壤吸附系数Ｋｏｃ，并研究了Ｋｏｃ与正辛醇一水分配系数Ｋｏｗ、水溶解度Ｓ之间的相关性，建立了相关方程式。     

餐饮业油烟气排放量的重量法监测

通过试验提出了一种监测餐饮业油烟气的方法，方法首次以单位体积油烟气中总有机物的质量来表示油烟气在空气中的浓度。通过试验选择了最佳监测条件；以石油醚作吸收剂，用ＫＢ－６Ａ型或ＴＭＰ－１５００型空气采样器，控制流量在０．４Ｌ／ｍｉｎ，采集油烟气，并在４０℃时挥发掉吸收剂，经称量得到监测结果，该法所需仪器和试剂简单，并在实际监测中得到了预期的结果。     

γ—BHC在黑暗,紫外光和日光下的异构化

γ－ＢＨＣ在黑暗，紫外光和日光下都能迅速地消失，紫外光照射下消失得最快，日光下次之，黑暗下较强，黑暗下未检出γ－ＢＨＣ的异构化产物。紫外光和日光均能促进γ－ＢＨＣ的异构化。异构化的主要产物为α，δ－ＢＨＣ，它们的异构化率性０．０４％～０．１７％之间。     

环境质量评价中模糊综合评判法,灰色聚类法及物元分析法的比较

比较了环境质量评价中应用的模糊综合评判法，灰色聚类法及物元分析方法的异同，着重比较了隶属函数，白化函数及关联度函数其物理意义上的差异，指出了今后在环境质量评价中应用这些方法应考虑的问题。     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

Neural networks and periodic components used in air quality forecasting

Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in urban areas. The work presented here aims at comparing two principally different neural network methods that have been considered as potential tools in that area and assessing them in relation to regression with periodic components. Self-organizing maps (SOM) represent a form of competitive learning in which a neural network learns the structure of the data. Multi-layer perceptrons (MLPs) have been shown to be able to learn complex relationships between input and output variables. In addition, the effect of removing periodic components is evaluated with respect to neural networks. The methods were evaluated using hourly time series of NO₂ and basic meteorological variables collected in the city of Stockholm in 1994–1998. The estimated values for forecasting were calculated in three ways: using the periodic components alone, applying neural network methods to the residual values after removing the periodic components, and applying only neural networks to the original data. The results showed that the best forecast estimates can be achieved by directly applying a MLP network to the original data, and thus, that a combination of the periodic regression method and neural algorithms does not give any advantage over a direct application of neural algorithms.     

基于改进型灰色神经网络组合模型的空气质量预测

基于空气质量数据不足及波动较大的情况,将灰色GM（1,1）模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM（1,1）模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。     

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.     

Predicting Sample Source Location from Soil Analysis Using Neural Networks

A system combining a national soils database with a neural network was developed for prediction of source location for soil samples. The neural network was trained to predict environmental characteristics, which can be of crucial importance to investigating officers in a police operation or to environmental agencies attempting to locate the source of a pollutant. When coupled with maps of environmental conditions and a generalized opinion pool approach, the system was used to produce weighted maps of source location. The system was capable of reducing search areas of a sample source to less than 0.1% of the total area.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Fuzzy neural network for flow estimation in sewer systems during wet weather.

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.     

Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy,USA

Environmental Science and Pollution Research - This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In...     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.