基于过程改变法的铁厂节水调控优化研究

将过程改变法应用到铁厂用水系统改造中,对其用水网络进行调优,实现废水排放量最小化。在厂区用水情况调查基础上,确定用水系统范围及限制杂质;并计算所确定用水系统的最小新鲜水用量和最小废水排放量;最后对其用水网络进行优化设计。改造后的铁厂用水网络新鲜水用量节约57.7%,废水排放量减少75.9%。     

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。     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

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.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

乡村路网对三峡库区社区水平森林结构的影响

道路作为人为扰动介质被认为是短时空尺度森林景观演化或用途转换的主要驱动力之一,体现为明显的“通道-阻隔”效应。论文以重庆市石柱县西沱镇为例,以3期遥感影像和调研数据为基础,利用ArcGIS的缓冲叠加功能,基于路网动态变化角度,从类型、林龄、起源等主要森林结构方面分析路网变化对森林结构变化的影响。结果表明：1）研究区道路数量先增加后减少,道路质量以等外公路和村级公路的改善为主。2）道路对森林景观干扰性明显。森林从类型、起源、林龄三方面在道路缓冲区内的破碎度均高于非缓冲区内的破碎度。1992—2002年及2002—2014年两个时期相比,前一时期其他灌木林、幼龄林以及萌生林大幅减少,基本表现为道路缓冲区内减少量大于非缓冲区内减少量。后一时期乔木林、中龄林以及萌生林有所增加,其他灌木林和成熟林轻微减少,其中,乔木林在道路缓冲区内增加量大于非缓冲区内增加量,中龄林及萌生林则表现为道路缓冲区内增加量弱于非缓冲区内增加量。3）道路影响域范围内,在无政策性干预条件下,道路的增加和质量的提高很大程度上导致森林景观的退化,低级路段的消失则有助于森林景观的恢复。     

“十二五”地表水国家环境监测网络吉林省水环境国控监测点位布设的构想

根据国家环境质量监测网络,对“十二五”地表水环境质量监测断面调整工作要求,吉林省在辖区内所有水环境质量监测断面及采样点基础上,完成了松花江流域和辽河流域国控监测断面的优化调整工作。在此过程中,如何从国家尺度上,既能充分反映吉林省水环境质量状况,又能满足国家对地表水环境管理需求进行了深入思考。本文以吉林省为例,在确定国控监测断面及采样点的布设中．提出了自己的构想和建议。     

新乡市镉污染土壤细菌群落组成及其对镉固定效果

重金属污染土壤中具有丰富的微生物群落组成,为生物修复提供了微生物资源.本研究以新乡市某电池厂周边Cd污染土壤为研究对象,采用高通量测序和平板分离方法分析其细菌群落组成.传统培养方法表明新乡市重金属污染土壤细菌由厚壁菌门、放线菌门、变形菌门和拟杆菌门这4个门,芽孢杆菌属、节杆菌属和根瘤菌属等30个属组成;高通量测序表明,其由变形菌门、放线菌门和酸杆菌门等25门及400属组成.相较于培养方法,高通量测序群落组成更为丰富.基于高通量测序的分子生态网路分析表明其关键细菌分别为Arthrobacter、Marmoricola、Nocardioides、Ferruginibacter、Flavitalea、Nitrospira和Lysobacter等组成.对分离的159株可培养菌株进行Cd摇瓶吸附实验,结果表明Aneurinibacillus、Arthrobacter和Bacillus等11个属的30株菌具有较好地固定效果.效果验证实验表明,Ochrobactrum sp. 1-6、Bacillus sp.2-11和Pseudomonas sp.1-9等6株高效菌株能提高青菜(鸡毛菜)生物量、降低...     

基于BP神经网络的北京城区公园土壤PAHs含量预测

城市公园是城市生态环境的重要组成部分,其环境质量与人类健康息息相关.选择北京市121个城区公园,采集公园土壤样品并分析其中7种多环芳烃（PAHs）含量,评价城区公园土壤中PAHs的含量水平,并基于BP神经网络预测了2020年和2023年土壤PAHs含量.结果表明:北京城区公园土壤中w（PAHs）（7种PAHs总含量）范围为0.033~4.182 mg/kg,低于GB 36600—2018《土壤环境质量建设用地土壤污染风险管控标准（试行）》土壤污染风险筛选值,且7种PAHs的毒性当量浓度（TEQ）均低于世界卫生组织标准值（1 mg/kg）,对人体健康的毒性风险较小.将14个影响指标（8个社会经济因子与6个公园特征因子）作为输入层、土壤w（PAHs）作为输出层,建立BP神经网络的拟合优度达0.845.预测结果显示,2020年和2023年北京城区公园土壤中w（PAHs）范围分别为0.008~0.969 mg/kg和0.022~1.988 mg/kg,整体均低于GB 36600—2018土壤污染风险筛选值,但随时间推移呈上升趋势,尤其朝阳区和海淀区将有大幅增长.研究显示:城市化发展因素对土壤w（PAHs）的增加有明显影响,城市发展进程影响不容忽视;至2023年,北京城区公园土壤若不加管理,其w（PAHs）将持续增长.      

BP 神经网络模型在环境气象灾害预测中的应用研究--以湖南省为例

依据环境气象数据与自然灾害统计数据,建立BP神经网络模型,对湖南主要气象灾害（洪灾、旱灾、冰冻灾）及受灾经济损失进行实例预测,将在MATLAB7软件中的仿真结果与传统的多元线性回归模型分析结果进行比较和误差分析。结果表明,BP神经网络模型在洪灾、旱灾受灾率方面的预测效果和精度优于多元回归模型,而由于冰灾训练样本不足及经济损失与输入因子的线性相关程度高,在冰灾与受灾经济损失率方面稍逊于多元回归模型。