Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.     

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor(ATFBR) with a mesoporous granular activated carbon(GAC)as a support material.The experimental protocol was defined to examine the effect of the maximum organic loading rate(OLR),hydraulic retention time(HRT),the efficiency of the reactor and to report on its steady- state performance.The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m~3.d).The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m~3/(m~3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m~3.d),the COD removal efficiency decreased.Also an artificial neural network(ANN) model using multilayer perceptron(MLP)has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data,the experimental values obtained have been used.The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data.The mean square error(MSE)was found to be only 0.0146.     

用循环经济理论指导工业区环境影响评价

区域开发的不确定性等给区域开发的环境影响评价(REIA)提出了较高的要求。循环经济按照自然生态学的原理,形成"资源-产品-消费-再生资源"的物质闭循环的流动过程。融入循环经济理念,将弥补传统区域开发环境影响评价模式的不足,更好发挥其在项目审批及环境管理中的作用。     

炼铝作业氟化物接触剂量与发病关系的分析

目的 了解炼铝作业氟的危害状况,为工业性氟病的防治提供依据.方法 对氟作业环境作职业卫生学调查.采用剂量患病率寿命表法对866名电解铝作业工人工业性氟病的发病情况进行流行病学调查,并作相关回归分析.结果 该厂接氟工人工业性氟病的总患病率为1.73%,且患病率随累积接氟量的增加而升高,呈高度正相关(r＝0.997,P＜0.01).并据所求得回归方程(y＝10.34InD-18.21)推算出接氟工人控制患病率时的允许连续工作年限和今后的发病趋势.结论 该厂电解铝车间氟危害较重,应加强卫生防护措施,并建议将车间氟最高容许浓度从目前1 mg/m3降为0.5 mg/m3为宜.     

基于BP神经网络的上市公司CEO报酬实证研究

近年来的研究表明,CEO报酬不仅受公司业绩的影响,还受到其他诸多因素的影响,而且,CEO报酬与其决定因素之间往往存在着非线性关系。本文以2003-2005年沪深股市的A股上市公司为样本,采用BP神经网络对CEO总报酬、CEO年薪、CEO持股价值及其决定因素分别进行训练和学习,结果表明：（1）网络训练输出值与实际值的拟合度分别达到91.09%、97.23%和78.44%;（2）网络的预测能力相对于传统的线性回归模型分别提高了92.72%、92.08%和53.89%。因此,本文认为在分析和确定CEO报酬水平时引入神经网络模型是可行的。     

城市工业区环境系统中的Cd污染循环及其健康风险——以杭州市半山工业区为例

为揭示重金属元素Cd在城市环境系统多种介质中的迁移途径及其潜在危害性,系统分析了杭州市半山工业区主要环境介质（土壤-尘埃-水-底泥-生物）样品中Cd的指标含量,并用连续提取法分析了土壤、尘埃、底泥中Cd的化学形态特征.结果表明,Cd的迁移途径主要有两种,一是以尘埃为媒介在地表环境中迁移,二是通过水介质进入水体环境（水和...     

Environmental and economic assessment of a greenhouse waste heat exchange

As the economic costs of energy and the negative externalities associated with the combustion of fossil fuels threaten the economic viability of greenhouses in northern climates there is a renewed interest in the use of waste heat. This paper presents a technical and economic methodology to determine the viability of establishing waste heat greenhouses using the waste heat from industrial processes in northern climates. A case study is presented of an exchange between a tomato greenhouse and a flat glass manufacturing plant, which found the waste heat system is significantly more economic to operate than a purely natural gas system.     

基于STIRPAT模型的能源消费碳足迹变化及影响因素——以江苏省苏锡常地区为例

定量研究经济社会发展对地区能源消费碳足迹的影响对区域实现低碳发展具有重要意义。论文计算了江苏省苏锡常地区1991-2008年能源消费碳足迹,采用岭回归函数对STIRPAT模型进行了拟合,采用脱钩指数分析了经济发展与能源消费碳足迹之间的关系。结果表明:①1991-2008年能源消费碳足迹平均增长速度为15.30%,能源消费碳足迹分配率以煤炭为主,石油所占比例呈波动下降趋势,天然气所占比例上升较快,能源消费碳足迹产值总体呈波动下降趋势;②经济增长是能源消费碳足迹的主要影响因素,两者关系模型拟合未出现环境库兹涅茨曲线;③经济增长与能源消费碳足迹之间处于相对脱钩与复钩的波动状态,从另一侧面验证了目前两者之间不存在库兹涅茨曲线假说的结论。     

Preparation and coagulation efficiency of polyaluminium ferric silicate chloride composite coagulant from wastewater of high-purity graphite production

The aim of the present work was to produce a polyaluminium ferric silicate chloride (PAFSiC) coagulant from acidic and alkaline wastewater of purifying graphite by roasting, and subsequently to evaluate coagulation efficiency of the reagent by treating surface water from the Yellow River as well as municipal wastewater in comparison with the conventional coagulant polyaluminium chloride (PAC). The PAFSiC coagulant was prepared by co-polymerization. The effects of (Al+Fe)/Si molar ratio, OH/(Al+Fe) molar ratio (i.e., value), coagulant dosage and pH value of test suspension on the coagulation behavior of FAFSiC and the stability of the PAFSiC were also examined. Results showed that PAFSiC performed more efficiently than PAC in removing turbidity, chemical oxygen demand (COD), and total phosphate (TP). The PAFSiC with a value of 2.0 and (Al+Fe)/Si ratio of 5 (PAFSiC 2.0/5) showed excellent coagulation effect for both turbidity and COD, while PAFSiC 1.0/5 was the best for TP. The optimum coagulation pH range of PAFSiC 2.0/5 was 5.0–9.0, slightly wider than that of PAC (6.0–8.0). The process can be easily incorporated into high-purity graphite production plants, thereby reducing wastewater pollution and producing a valuable coagulant.     

供水管网水质评价方法的探讨及其应用

利用了模糊数学评价模型和综合指数评价模型分别对南方某市部分给水管网水质进行评价。评价结果分析表明,模糊数学评价法具有评价准确性好、操作简单的特点;综合指数法具有评价结果直观、精确度较高的优点。从评价结果上看,综合指数法较模糊数学评价法更为合理。