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.     

非等间隔GM（1，1）模型在液相色谱保留值研究中的应用

本文运用灰色系统理论思想，建立非等间隔ＧＭ（１，１）模型，对色谱中的流动相组成及容量因子的关系进行预测研究，所建立模型能实现对各实验点值进行模拟和合理地预测。     

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.     

关中地区清洁能源供暖综合效益评价——西安某商业建筑的案例实证

随着中国发展方式转变与能源结构转型的深入推进,能源消耗“双控”与燃煤替代供热清洁化、集中化成为必然趋势。清洁供暖效益评价研究是一个动态的系统性问题,涉及技术、财务、经济和社会评价等方面。目前,有关供热模式评价一般集中在经济和技术视角,难以系统反映经济社会、环境资源等深层次问题。以空气源热泵、燃气锅炉和浅层地热能为主多能互补的供暖方案为评价对象,采用系统评价的方法,基于DPSIR模型构建清洁能源供暖综合效益评价指标体系,利用AHP-POS灰色关联度模型定量评价西安市商业建筑清洁能源供暖方案综合效益和D-P-S-I-R子系统的影响作用。结果表明：在经济、技术和投资环境等条件允许情况下,应优先实施以浅层地热能为主的多能互补供暖方案,依次实施煤改电和煤改气方案。进而提出关中地区清洁能源供暖监管体系对策建议,以供决策者和投资者参考。     

H2S脱除效率主控因素的广义灰色关联分析

为更好保护井下人员、设备安全,为井下H2S治理提供基础实验依据,通过脱除H2S气体实验装置,改变装置内的风速、NaHCO3溶液质量分数、H₂S气体浓度来探究影响脱除H2S效率的主控因素。选取部分数据并基于灰色关联度分析,对变量的重要度进行排序。计算结果表明:风速、NaHCO3溶液质量分数、H2S气体浓度的综合关联度分别为0.568,0.543,0.541 5,并通过这3种因素的综合分析,得出影响H2S脱除效率因素大小顺序为:风速＞溶液质量分数＞H2S气体浓度;表明一定条件下,H2S脱除效率随风速及H2S气体浓度的增大而降低、随溶液质量分数增大而增大的规律。     

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。     

基于熵权灰靶生态系统服务价值模型的土地利用规划环境影响评价——以晋城市为例

生态系统服务功能价值法是土地利用规划环境影响评价中应用较为广泛的方法之一,然而其存在不同研究区域之间可比性较弱的问题,为增强可比性,本次研究引入灰靶理论及熵权理论,构建了熵权灰靶生态系统服务价值模型,并将其应用于山西省晋城市之中.结果表明,在规划目标年,晋城市下辖各区县中,城区、高平市、阳城县、泽州县、陵川县及沁水县的靶心度分别为0.3354、0.3913、0.4962、0.5020、0.5521及1.0000,进而基于上述结果,对晋城市下辖各区县提出了差异化的土地利用意见建议.本次研究成果从理论研究与实际工作方面都对晋城市及其它地区土地利用规划环境影响评价提供了一定的参考.