基于最优组合预测模型的中国工业固体废物产生量预测

随着经济的发展,工业固体废物的产生量迅速膨胀,已成为制约社会和经济持续健康发展的瓶颈。为了客观地反映中国工业固体废物的发展趋势,在神经网络和逐步回归2种单项预测方法的基础上,建立了最优组合预测模型。并以1991—2007年中国工业固体废物产生量为基础数据,通过实例对模型精度进行分析,预测值与实测值具有良好的一致性。由于非线性最优组合预测模型综合考虑了神经网络对历史数据拟合效果良好和逐步回归对预测对象影响因素考虑全面的优点,得到的结果误差较小,最符合实际情况。采用非线性最优组合预测模型预测未来几年中国工业固体废物的产生量,结果显示,2010年中国工业固体废物产生量将达到183133万t,2020年将达到228511万t,预测结果可以为中国工业固体废物管理提供科学依据。     

单块集尘板振打加速度的有限元分析

采用有限元分析软件ABAQUS,对单块集尘板振打加速度进行了分析计算。并将计算结果与解析法和实测法结果进行对比。对比结果表明,有限元计算值更接近于实测值。用有限元法对某厂实际运行的电除尘器加速度进行求解,计算结果与实际基本吻合。由于有限元法能非常方便地处理各种复杂板型、不同的悬挂方式、振打锤的大小以及振打方式,因此,有限元法对于现有极板的分析与新型极板的研究均具有重要的实际意义。     

污染地块土壤砷与苯并[a]芘生物可给性影响因素研究与模型预测

利用模型预测生物可给性比现场采样测试获取生物可给性检测结果耗时短、成本低。为探究砷 (As) 和苯并[a]芘 (BaP) 生物可给量与土壤性质之间的关系,统计了12篇文献和3份风险评估报告中As和BaP生物可给性、生物可给量和土壤性质数据,分析了生物可给量与土壤性质之间的关系,并基于逐步回归分析法和随机森林回归法构建了生物可给量的预测模型。结果表明：土壤中As的生物可给量与总量呈极显著正相关 (P<0.01) ,与土壤pH和CEC呈极显著负相关,BaP的生物可给量与总量呈极显著正相关,与土壤pH和粘粒质量分数呈极显著负相关性;分别采用逐步回归法和随机森林回归法构建了As和BaP生物可给量预测模型,综合比较2种模型训练集和测试集的R²大小,发现随机森林回归预测模型对生物可给量的预测结果优于逐步回归预测模型,且随机森林预测模型特征重要性与相关性分析结果一致;采用随机森林回归预测模型进行案例地块验证,验证结果表明,随机森林回归预测模型对6个典型污染地块As和BaP的生物可给量预测效果较好 (R²=0.97) 。研究结果可为重金属和半挥发性有机物污染地块中生物可给性的应用提供技术支持。     

高架连续点源落地浓度预测中风速的确定

高架连续点源污染物排放落地浓度是大气环境影响预测的主要内容。由于大气污染物扩散明显受气象条件尤其是风速的影响,而现有预测模型中对于风速的取值都是按经验值来确定的。通过分析在不同气象和烟源条件下,平均风速的计算方法对烟羽抬升高度以及最大落地浓度产生的影响,与实测值相比较确定了风速取值的合理方法,缩小了预测偏差。     

公路交通噪声预测模型FHWA与RLS90的比较

为了了解我国长期以来使用的两种公路交通噪声预测模型,即美国联邦公路局(FHWA)模型和德国RLS90模型的预测精确性,通过理论分析,比较了RLS90分段模型、RLS90长直线模型、FHWA模型的差异,并通过实际测量值对3种模型的计算结果进行了验证,表明RLS90模型在高速公路交通噪声预测中略优于FHWA模型。     

基于BP人工神经网络的城市PM2.5浓度空间预测

针对PM2.5日均质量浓度,采用BP人工神经网络模型,预测研究区空气中PM2.5浓度的空间变异,通过与普通克里格(Ordinary Kriging)插值方法对比验证BP人工神经网络预测模型的精度.结果表明:BP人工神经网络预测模型下研究区检验样本点位置的PM2.5仿真浓度与观测浓度之间的均方差、平均绝对误差、平均相对偏差和相关系数分别为0.296 μg2/m6、0.412 μg/m3、1.650％和0.851;而与此同时,普通克里格插值方法下的对应结果分别为1.041 μg2/m6、0.689 μg/m3、11.910％、0.638.研究成果在肯定BP人工神经网络预测模型可用于揭示PM2.5浓度空间变异特征的同时,也证实了其相对于普通克里格插值方法在固定空间点位准确预测PM2.5浓度方面的优势.     

高架道路声屏障的降噪效果

详细介绍了德国RLS 90道路交通噪声预测模型,运用RLS 90模型对具有典型道路参数的高架道路试验段在不同距离、不同高度下进行了辐射噪声和声屏障降噪效果特性的预测计算,并进行了实际测量。结果表明,高架道路对低于其路面高度的近距离区域有显著的噪声遮蔽作用,对于高架道路,声屏障对路面高度附近的区域降噪效果最佳,4 m高声屏障的最大降噪效果达10 dB。结果还表明,采用RLS 90模型得到的高架道路噪声级和声屏障降噪效果计算值和实测数据吻合良好。     

广东省道路交通噪声预测统计模型

为了建立符合广东省的道路交通噪声预测模型,在国内学者相关研究成果的基础上,对FHWA模型进行了深入分析,并根据道路交通噪声预测模型原理,引入等效车流量等概念建立了理论噪声预测统计模型.在此基础上,对广东省市政路和高速路的交通噪声实测数据进行多元线性回归分析,确定广东省交通噪声预测统计模型.经验证,在广东省交通噪声预测评价中该模型预测精度非常高.该研究思路与方法值得在全国各地区处理交通噪声环境问题的工作中加以推广与应用.     

深圳市区空气污染的人工神经网络预测

利用深圳市2006至2013年的大气污染物监测浓度数据和气象资料,分析深圳市空气质量的逐月分布变化特征。采用Pearson相关分析,选择显著相关因子,分别以BP神经网络和RBF神经网络构建空气质量预测模型,对该市2013年SO2、NO2、PM103种空气污染物的月均值进行预测。实验结果表明,通过Pearson相关分析建立的预测模型有更高的预报精度。BP和RBF 2种网络预测效果都比较理想,对不同污染物的预测精度各有高低。但BP网络的构建和参数优化过程较为复杂且网络训练结果不稳定,而RBF网络构建和训练简单,时间短而结果稳定。在综合性能上,RBF网络用于环境空气污染物浓度的预测具有更强的适用性。     

关于大气污染预测方法的新探讨

本文在风速、大气稳定度联合频率和各种稳定度条件下混合层高度的基础上,用积分均值法修正的高斯模式和箱式模式分别计算石家庄市点源和面源排出的SO_2在春、夏,秋,冬四季的长期平均浓度,计算值和实测结果比较吻合,从而验证了本方法在城市和经济开发区对大气环境预测及污染防治的实用性。并结合气候、逆温等因素对计算结果进行了分析。     

道路交通噪声预测的研究进展

为评价道路交通噪声影响情况,以美国联邦公路管理局(FHWA)噪声预测模型为主,各国相继开发了基于当地实际情况的道路交通噪声预测模型,中国也以导则和规范等形式相继发布了各种交通噪声预测模型。中国进行噪声环境影响评价中,用得较多的主要有《环境影响评价技术导则声环境》(HJ 2.4—2009)、《公路建设项目环境影响评价规范》(JTG B03—2006)推荐的噪声预测模型,此外也有部分评价工作采用了德国的Cadna A软件。通过对比分析,HJ 2.4—2009的衰减及修正因素在综合考虑FH-WA噪声预测模型及JTG B03—2006基础上更加全面,而JTG B03—2006和Cadna A软件在源强和车速确定方面则均较成熟。有必要根据区域道路特点,选择适合特征区域的道路交通噪声预测模型,并结合实际情况对模型参数进行合理修正,最终形成一些可供区域参考的统一的模型修正参数,积极推动中国的声环境评价工作的发展。     

Traffic noise exposure of high-rise residential buildings in urban area

Noise pollution is a major factor of environmental complaints in many cities, which has significant impacts on human health. As a dominating source of environmental noise, the impact of road traffic noise is increasing. Residents living in high-rise buildings along the main road are severely affected by traffic noise. In order to assess the noise level of urban area along the main road in Guangzhou, three buildings were selected to conduct traffic noise measurements, and the questionnaire about traffic noise impact on human being was completed. Through the questionnaire, around 70% of participants consider the traffic noise has negative effect, and about 60% of participants consider the noise has moderate or much higher impact on physical comfort. Around 65% of participants consider the noise had moderately or much higher impact on their psychological comfort. By analyzing the measured data, all of the measured noise levels in three buildings exceed the recommended limit of 55 dB (A) in the daytime and 45 dB (A) in the night for residence, and the exceeded value can be up to 16 dB (A). By comparing the fitting curve of noise level transfer function on each floor relative to the reference floor, the quadratic polynomial was selected to plot the transfer function rather than cubic polynomial.

     

Prediction of Traffic Noise: A Screening Technique

Abstract

Traffic noise is ubiquitous in many communities and is an important environmental concern, especially for persons located near major roadways. Several different methods are available to estimate noise levels resulting from roadway traffic. These include computational, graphical, and computer modeling techniques.

The prediction methodology presented here is a simplified technique that can be used for estimating noise resulting from traffic and for screening traffic noise impacts. This Traffic Noise Screening (TNS) approach consists of a series of traffic noise level prediction graphs developed for different roadway configurations. The graphs are based on the results from using the Federal Highway Administration (FHWA) STAMINA2.0 computerized noise prediction model for various scenarios. Data inputs to the TNS approach include roadway geometries, traffic volumes, vehicle travel speed, and centerline distance to the receptors.

The TNS graphs allow easy estimation of traffic noise levels for use in predicting traffic-related noise impacts. This TNS approach is not intended as a substitute for detailed modeling, such as with STAMINA2.0, but as a screening tool to aid in determining when detailed modeling may be necessary. If screening results indicate that noise estimates are significant, or if the scenario is rather complex, then additional, more detailed modeling can be performed.     

夏冬两季城郊公路绿化带的降噪效果差异分析

在夏冬两季监测柳州市柳长路绿化带的噪声值,分析全乔型、乔灌结合型、全灌型3种标准配置方式的典型绿化带降噪效果季节差异。结果表明：各类型绿化林带对城郊公路的交通噪声都有一定的降噪效果,能达到标准要求,但不同配置方式降噪效果不同;从夏冬两季绿化林带降噪效果分析,整体上夏季的降噪效果强于冬季,全灌木型夏冬季的降噪效果接近,乔灌结合型夏冬季降噪值差值为2.24~3.16 dB,全乔木型差异最明显,夏季降噪量比冬季大3.84 dB;在30 m绿化带范围内,0~10 m处可以得到很稳定的降噪量。     

Impacts of pavement types on in-vehicle noise and human health

Noise is a major source of pollution that can affect the human physiology and living environment. According to the World Health Organization (WHO), an exposure for longer than 24 hours to noise levels above 70 dB(A) may damage human hearing sensitivity, induce adverse health effects, and cause anxiety to residents nearby roadways. Pavement type with different roughness is one of the associated sources that may contribute to in-vehicle noise. Most previous studies have focused on the impact of pavement type on the surrounding acoustic environment of roadways, and given little attention to in-vehicle noise levels. This paper explores the impacts of different pavement types on in-vehicle noise levels and the associated adverse health effects. An old concrete pavement and a pavement with a thin asphalt overlay were chosen as the test beds. The in-vehicle noise caused by the asphalt and concrete pavements were measured, as well as the drivers’ corresponding heart rates and reported riding comfort. Results show that the overall in-vehicle sound levels are higher than 70 dB(A) even at midnight. The newly overlaid asphalt pavement reduced in-vehicle noise at a driving speed of 96.5 km/hr by approximately 6 dB(A). Further, on the concrete pavement with higher roughness, driver heart rates were significantly higher than on the asphalt pavement. Drivers reported feeling more comfortable when driving on asphalt than on concrete pavement. Further tests on more drivers with different demographic characteristics, along highways with complicated configurations, and an examination of more factors contributing to in-vehicle noise are recommended, in addition to measuring additional physical symptoms of both drivers and passengers.Implications: While there have been many previous noise-related studies, few have addressed in-vehicle noise. Most studies have focused on the noise that residents have complained about, such as neighborhood traffic noise. As yet, there have been no complaints by drivers that their own in-vehicle noise is too loud. Nevertheless, it is a fact that in-vehicle noise can also result in adverse health effects if it exceeds 85 dB(A). Results of this study show that in-vehicle noise was strongly associated with pavement type and roughness; also, driver heart rate patterns presented statistically significant differences on different types of pavement with different roughness.     

Traffic atmospheric diffusion model

A digital computer model simulation of traffic flow using car-following theory and constrained random input has been developed to predict the concentration of gaseous pollutants (i.e. NO₂ and HC) in the atmosphere emitted from different types of vehicles on the roads, using a simple Gaussian dispersion point source plume.Measurements were made on different roads of Baghdad city. Atmospheric pollutant concentrations, traffic flow rate, traffic composition and climatic conditions were recorded.The prediction method was then tested against measurements. The predicted levels are in acceptable agreement with the measured values. The model was then used to examine various traffic and pollutant control strategies.     

Effect of Plantings on Radiation of Highway Noise

The body of information presented in this paper is directed to those individuals concerned with the location of highways relative to either existing or planned residential communities. The paper treats in depth the expected attenuation of automobile and particularly diesel tractor-trailer generated hoise by the interposition of extensive and dense planting of trees and shrubs between the highway and the community. The results of research, by the author and others cited in the paper, gives clear indication that a belt of dense man-made growth of tall trees and underbrush can give as much as 5 to 8 dB truck/car noise reduction per 100 ft of planting depth. Natural growth of deep forests were measured to give from 3 to 5 dB attenuation per 100 ft of planting depth. Planting depth of at least 100 ft is required to give reliable results, with tree heights of 40-50 ft desirable and densities of 50-70 ft visibility needed for good effect.

The paper provides considerable experimental verification by an analysis of the current literature. The types of noise sources in cars and trucks are briefly treated as are community reactions to noise from such sources. The dBA is used as the accepted measure for characterizing truck and automobile noise. Both spherical and cylindrical radiation of sound are discussed for low density and high density traffic, respectively.

The paper concludes with an example of attenuation of a typical truck noise by spherical and cylindrical radiation for low- and high-density traffic and a 200-ft deep planting of dense, mature forest. It is concluded that a mature belt of either coniferous or deciduous forest with underbrush can produce barely acceptable noise levels in the community which is separated by such a noise barrier from a heavily traveled highway.     

城市天然气加气站噪声影响分析及噪声控制技术简

城市天然气加气站位于城市道路和居民区周边。由于加气站地理位置特殊,声源复杂,与周边道路对环境的影响叠加,还要考虑通风散热等问题,噪声治理难度大。目前,国内关于城市天然气加气站的噪声控制研究的报道较少。本研究以合肥市长江西路天然气加气站为例,根据城市天然气加气站加气工艺噪声源及邻近交通噪声对周围环境进行叠加影响预测,根据预测结果和通风散热等工艺要求设计冷却塔安装进风、出风消声器和隔声罩,压缩机安装局部通风隔声罩及压缩机房东墙内安装隔声墙及通风隔声窗等措施。经检验,研究结果好于《工业企业厂界噪声排放标准（GB12348-2008）》中的2类标准的夜间50 dB（A）的标准。该研究设计技术工艺、参数先进合理,费用低且实际简单,为国内天然气加气站噪声预测及治理提供较好的示范作用。