海口市声环境影响因素分析及预测

噪声污染一直是海口市主要的环境问题之一。主要原因是城市纵深度太低，道路密度太高，交通布局不合理，1991年－2000年城市区域环境噪声和道路交通噪声的平均值分别为59．0dB（A）和69．5dB（A）。利用城市区域环境噪声预测方法和道路交通噪声预测方法对该市噪声进行预测，2001年－2005年该市的区域噪声昼间平均等效声级综合预测值在57．6dB（A）－56．7dB（A）之间；道路交通噪声昼间平均等效声级综合预测值在68．2dB（A）－68．3dB（A）之间。     

PREDICTING CHANGE IN NON-LINEAR SYSTEMS

Complex systems are characterizedby surprising switches to new behaviours. Evaluating and predicting these changes demands anunderstanding of the behaviour of the whole system. The combined ecosystem-climate system shows chaoticor pseudorandom behaviour, stochastic or trulyrandom behaviour, as well as simple bifurcation andsemi-stability. Semistability involves the suddenchange from a destabilized attractor to a newstable attractor which may occur after an apparentlyunpredictable time delay. We present some recentresults for analyzing time series data and for usingsimulations of non-linear models to predict these changes.     

Modelling of Motor Vehicle Fuel Consumption and Emissions Using a Power-Based Model

The performance of a power based fuel consumption and exhaust emissions model for spark ignition vehicles has been evaluated using a large Australian database derived from testing a wide range of in-use cars on a chassis dynamometer. It was also applied to results of on-road fuel consumption measurement using a "floating" car which was driven back and forth on hilly roadways in Sydney with a length of 8.6 km. The model is found to predict the fuel consumption well over the standard drive cycles and also for the floating car. Average exhaust emissions were also well predicted, but, as would be expected, vehicle-to-vehicle correlation is impossible due to the well-known high variability of emissions between nominally identical vehicles.     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

中国废纸回收系统物质流分析与评价

构建了2017年中国废纸回收决策系统的基准模型,其中重点关注废纸非规范回收对中国国内废纸回收系统的经济效益和环境（GHG排放量）的影响.其次,对影响废纸回收系统效益的相关参数进行了灵敏度分析,最后在整合非规范回收商贩情境下对系统经济效益和GHG排放量进行了预测.结果表明：2017年中国废纸回收的经济效益约为458.3元/t,GHG排放量为901.1kgCO_2eq;规范回收率和非规范回收接受率都会对系统经济效益和改善GHG排放结构有显著影响;整合非规范回收企业以及个体回收商的情境下2030年经济效益将上升至3312.5元/t,而GHG排放量上升至942.9kgCO_2eq.并且通过情境预测发现整合非规范回收能提升规范回收率,这能有效的规范中国的废纸回收市场.     

北京城市塑料垃圾年产量的模拟预测及其影响因素分析

揭示城市塑料垃圾年产量及影响因素、预测其发展趋势对于城市生活垃圾收集系统的优化、处理技术的合理选择和降低环境影响具有重要意义.本研究基于1989年以来北京塑料垃圾占比、城市生活垃圾产量数据和社会经济数据,利用赤池信息量准则（AIC）和灰关联度法研究了北京城市塑料垃圾占比的年变化趋势和城市塑料垃圾年产量的主要影响因素.通过多元线性回归模型（MLR）、灰色系统模型GM（1,1）和BP神经网络模型对北京城市塑料垃圾年产量进行了模拟预测.结果表明,北京城市塑料垃圾占比由1989年的1.88%,增加到2012年的14.87%.基于AIC准则预测2013—2050年北京城市塑料垃圾占比增长趋势较平缓、稳定在14%~19%之间.2000—2012年北京市城市塑料垃圾年产量由40.2×10⁴ t增加到121.1×10⁴ t,年增长15.5%.人均可支配收入是影响北京城市塑料垃圾年产量的最大社会经济因素,而常住人口的影响较低.BP神经网络是模拟预测北京城市塑料垃圾产量的最佳模型,其模拟预测结果表明：2013年后北京市塑料垃圾年产量随时间呈不规则的非线性增长趋势,到2025、2035、2050年北京城市塑料垃圾产量将分别达到335、488和859×10⁴ t,将对北京城市生活垃圾处理处置与防控管理带来巨大挑战.     

高家坪隧道岩溶水系统识别及涌水量预测

为查明在建宜保高速高家坪隧道内ZK45+995m处突水点的补给来源以及隧道区的水文地质条件,并预测可能的涌水量,在对隧道区内岩溶发育规律调查分析的基础上,先后在隧道区开展了两次地下水示踪试验,结果查明:高家坪隧道内ZK45+995m处突水点的补给来源于隧道北侧的下埫岩溶洼地,汇水面积为0.66km2,突水点所处的岩溶管道为黄龙洞岩溶水系统的西支,为单一岩溶管道类型,地下水最大流速为341 m/d,平均流速为244m/d,地下水流速快,管道介质相当发育。根据示踪试验划分的黄龙洞岩溶水系统与干洞坪岩溶水系统边界范围,并利用黄龙洞泉长期降雨量-泉流量监测数据,采用大气降雨入渗法预测高家坪隧道ZK45+995m处突水点未来可能遭遇的最大涌水量为13 216m3/d,正常涌水量为5 940m3/d,为隧道防治水方案的制定提供了水文地质依据。     

一种战车主减速器温度预测方法研究

目的针对战车主减速器温度预测需求,建立时间序列ARIMA多步预测和BP神经网络预测模型,提出基于BP神经网络修正误差的ARIMA模型温度预测方法。方法结合BP神经网络的非线性能力与ARIMA模型预测能力,分析ARIMA在多步预测时误差产生原因,在神经网络对ARIMA多步误差进行预测基础上计算修正因子,把误差修正因子和BP网络结合,实现对多步预测误差的修正。结果ARIMA模型多步预测时,预测误差随预测步数的逐步增加不断增大,引入了误差修正因子进行修正。通过预测值与实际值进行对比,可有效提高预测准确度。结论 BP神经网络和误差修正因子结合应用可显著提高温度预测效果。     

Towards Real‐Time Continental Scale Streamflow Simulation in Continuous and Discrete Space

The National Weather Service (NWS) forecasts floods at approximately 3,600 locations across the United States (U.S.). However, the river network, as defined by the 1:100,000 scale National Hydrography Dataset‐Plus (NHDPlus) dataset, consists of 2.7 million river segments. Through the National Flood Interoperability Experiment, a continental scale streamflow simulation and forecast system was implemented and continuously operated through the summer of 2015. This system leveraged the WRF‐Hydro framework, initialized on a 3‐km grid, the Routing Application for the Parallel Computation of Discharge river routing model, operating on the NHDPlus, and real‐time atmospheric forcing to continuously forecast streamflow. Although this system produced forecasts, this paper presents a study of the three‐month nowcast to demonstrate the capacity to seamlessly predict reach scale streamflow at the continental scale. In addition, this paper evaluates the impact of reservoirs, through a case study in Texas. Validation of the uncalibrated model using observed hourly streamflow at 5,701 U.S. Geological Survey gages shows 26% demonstrate PBias ≤ |25%|, 11% demonstrate Nash‐Sutcliffe Efficiency (NSE) ≥ 0.25, and 6% demonstrate both PBias ≤ |25%| and NSE ≥ 0.25. When evaluating the impact of reservoirs, the analysis shows when reservoirs are included, NSE ≥ 0.25 for 56% of the gages downstream while NSE ≥ 0.25 for 11% when they are not. The results presented here provide a benchmark for the evolving hydrology program within the NWS and supports their efforts to develop a reach scale flood forecasting system for the country.